Jeffrey Mak

Jeffrey Mak

Doctoral Student

Departmental Lecturer

Student, Keble College

E: fname.mname.lname@cs.ox.ac.uk

Wolfson Building, Parks Road, Oxford OX1 3QD

Themes:

• Computational Biology and Health Informatics

Interests

My research lies at the intersection between machine learning and CRISPR biology, where I use interpretable machine and deep learning to build CRISPR-Cas9 cleavage activity prediction tools. Since there are numerous biological factors beyond the spacer-target sequence pair which influence CRISPR-Cas9 cleavage activity, my work explores the use of such factors as computational input feature representations in these tools. By accounting for these factors, we hope to boost the utility of CRISPR-Cas9 activity prediction tools and ultimately advance the safety of genome editing.

Biography

Jeffrey completed his BEng in Computer Science at the University of Hong Kong and his MSc in Computer Science at Oxford. In 2019, he started his DPhil degree at Oxford titled "Structure-aware and interpretable machine learning for CRISPR-Cas9 cleavage activity prediction", due to be completed in 2026.

As graduate teaching assistant in 2019-2024 Jeffrey ran and led undergraduate practical labs for various modules, notably Databases and Design and Analysis of Algorithms. Jeffrey also taught Computational Biology classes. Jeffrey completed the Advanced Teaching and Learning programme at the Centre for Teaching and Learning in 2021, and was admitted as an Associate Fellow in the Higher Education Academy, UK. In 2024-2025, Jeffrey taught various first and second year core modules as a stipendiary lecturer at Keble College.

Selected Publications

View AllManage publications

• Learning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR–Cas9 off−target activity

  Jeffrey Kelvin Mak‚ Artemi Bendandi‚ José Augusto Salim‚ Ivan Mazoni‚ Fabio Rogerio de Moraes‚ Luiz Borro‚ Florian Störtz‚ Walter Rocchia‚ Goran Neshich and Peter Minary

  In NAR Genomics and Bioinformatics. Vol. 7. No. 2. Pages lqaf054. 2025.

  Details about Learning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR–Cas9 off−target activity | BibTeX data for Learning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR–Cas9 off−target activity

• piCRISPR: physically informed deep learning models for CRISPR/Cas9 off−target cleavage prediction

  Florian Störtz‚ Jeffrey K Mak and Peter Minary

  In Artificial Intelligence in the Life Sciences. Vol. 3. Pages 100075. 2023.

  Details about piCRISPR: physically informed deep learning models for CRISPR/Cas9 off−target cleavage prediction | BibTeX data for piCRISPR: physically informed deep learning models for CRISPR/Cas9 off−target cleavage prediction

• Comprehensive computational analysis of epigenetic descriptors affecting CRISPR−Cas9 off−target activity

  Jeffrey K Mak‚ Florian Störtz and Peter Minary

  In BMC genomics. Vol. 23. No. 1. Pages 805. 2022.

  Details about Comprehensive computational analysis of epigenetic descriptors affecting CRISPR−Cas9 off−target activity | BibTeX data for Comprehensive computational analysis of epigenetic descriptors affecting CRISPR−Cas9 off−target activity